Software Alternatives, Accelerators & Startups

AWS Batch VS Luigi

Compare AWS Batch VS Luigi and see what are their differences

AWS Batch logo AWS Batch

AWS Batch enables developers, scientists, and engineers to easily and efficiently run hundreds of thousands of batch computing jobs on AWS.

Luigi logo Luigi

Luigi is a Python module that helps you build complex pipelines of batch jobs.
  • AWS Batch Landing page
    Landing page //
    2023-02-21
  • Luigi Landing page
    Landing page //
    2023-10-08

AWS Batch videos

How AWS Batch Works

More videos:

  • Review - Live from the London Loft | AWS Batch: Simplifying Batch Computing in the Cloud
  • Review - AWS re:Invent 2018: AWS Batch & How AQR leverages AWS to Identify New Investment Signals (CMP372)

Luigi videos

Luigi's Mansion 3 Review

More videos:

  • Review - Luigi's Mansion 3 Review
  • Review - Luigi's Mansion 3 - REVIEW (Nintendo Switch)

Category Popularity

0-100% (relative to AWS Batch and Luigi)
Cloud Hosting
100 100%
0% 0
Workflow Automation
0 0%
100% 100
Cloud Computing
100 100%
0% 0
Workflows
0 0%
100% 100

User comments

Share your experience with using AWS Batch and Luigi. For example, how are they different and which one is better?
Log in or Post with

Reviews

These are some of the external sources and on-site user reviews we've used to compare AWS Batch and Luigi

AWS Batch Reviews

Python & ETL 2020: A List and Comparison of the Top Python ETL Tools
AWS Batch: This is used for batch computing jobs on AWS resources. It has insane scalability and is well-suited for engineers look to do large compute jobs.
Source: www.xplenty.com

Luigi Reviews

5 Airflow Alternatives for Data Orchestration
In this blog post, we will discuss five alternatives to manage workflows: Prefect, Dagster, Luigi, Mage AI, and Kedro. These tools can be used for any field, not just limited to data engineering. By understanding these tools, you'll be able to choose the one that best suits your data and machine learning workflow needs.
Top 8 Apache Airflow Alternatives in 2024
Even though Airflow and Luigi have much in common (open-source projects, Python used, Apache license), they have slightly different approaches to data workflow management. The first thing is that Luigi prevents tasks from running individually, which limits scalability. Moreover, Luigi’s API implements fewer features than that of Airflow, which might be especially difficult...
Source: blog.skyvia.com
10 Best Airflow Alternatives for 2024
Among a popular choice for an Apache Airflow alternative is Luigi. It is a Python package that handles long-running batch processing. This means that it manages the automatic execution of data processing processes on several objects in a batch. A data processing job may be defined as a series of dependent tasks in Luigi.
Source: hevodata.com
Python & ETL 2020: A List and Comparison of the Top Python ETL Tools
When does Luigi make sense? If you need to automate simple ETL processes (like logs) Luigi can handle them rapidly and without much setup. When it comes to complex tasks, Luigi is limited by its strict pipeline-like structure.
Source: www.xplenty.com
Comparison of Python pipeline packages: Airflow, Luigi, Gokart, Metaflow, Kedro, PipelineX
Luigi enables you to define your pipeline by child classes of Task with 3 class methods (requires, output, run) in Python code.
Source: medium.com

Social recommendations and mentions

Based on our record, AWS Batch should be more popular than Luigi. It has been mentiond 14 times since March 2021. We are tracking product recommendations and mentions on various public social media platforms and blogs. They can help you identify which product is more popular and what people think of it.

AWS Batch mentions (14)

  • Looking for a decent (self hostable) program to orchestrate scripts, notify on failures, etc
    After moving off Jenkins, I moved everything to AWS Batch with Fargate. This works quite well, but it is proving to be a little expensive, as I have to pay for:. Source: 12 months ago
  • Hosting strategy suggestions
    If you're looking for more control over your infrastructure and want to run a full computing environment, EC2 might be the right choice for you. With EC2, you have complete control over the operating system, network, and storage, which can be useful if you need to install custom software or use specific hardware configurations. Additionally, EC2 + Batch processing provide a wider range of instance types, including... Source: about 1 year ago
  • Questions for bioinformatics researchers that use AWS
    AWS Batch is the equivalent of a university cluster you submit to with slurm/sge/lsf/etc. But does not use those schedulers as AWS has their own. Source: over 1 year ago
  • Scheduling "Fetch & Run" Batch Jobs with AWS Batch and CloudWatch Rules
    Developers frequently use batch computing to access significant amounts of processing power. You may perform batch computing workloads in the AWS Cloud with the aid of AWS Batch, a fully managed service provided by AWS. It is a powerful solution that can plan, schedule, and execute containerized batch or machine learning workloads across the entire spectrum of AWS compute capabilities, including Amazon ECS, Amazon... - Source: dev.to / over 1 year ago
  • can you run OS applications in lambda layers?
    As others mentioned, you *can*. It might be easier with AWS Batch (https://aws.amazon.com/batch/) depending on what you're trying to do. Source: over 1 year ago
View more

Luigi mentions (9)

  • Ask HN: What is the correct way to deal with pipelines?
    I agree there are many options in this space. Two others to consider: - https://airflow.apache.org/ - https://github.com/spotify/luigi There are also many Kubernetes based options out there. For the specific use case you specified, you might even consider a plain old Makefile and incrond if you expect these all to run on a single host and be triggered by a new file... - Source: Hacker News / 9 months ago
  • In the context of Python what is a Bob Job?
    Maybe if your use case is “smallish” and doesn’t require the whole studio suite you could check out apscheduler for doing python “tasks” on a schedule and luigi to build pipelines. Source: almost 2 years ago
  • Lessons Learned from Running Apache Airflow at Scale
    What are you trying to do? Distributed scheduler with a single instance? No database? Are you sure you don't just mean "a scheduler" ala Luigi? https://github.com/spotify/luigi. - Source: Hacker News / about 2 years ago
  • Apache Airflow. How to make the complex workflow as an easy job
    It's good to know what Airflow is not the only one on the market. There are Dagster and Spotify Luigi and others. But they have different pros and cons, be sure that you did a good investigation on the market to choose the best suitable tool for your tasks. - Source: dev.to / over 2 years ago
  • DevOps Fundamentals for Deep Learning Engineers
    MLOps is a HUGE area to explore, and not surprisingly, there are many startups showing up in this space. If you want to get it on the latest trends, then I would look at workflow orchestration frameworks such as Metaflow (started off at Netflix, is now spinning off into its own enterprise business, https://metaflow.org/), Kubeflow (used at Google, https://www.kubeflow.org/), Airflow (used at Airbnb,... Source: over 2 years ago
View more

What are some alternatives?

When comparing AWS Batch and Luigi, you can also consider the following products

Nuclio - Nuclio is an open source serverless platform.

Apache Airflow - Airflow is a platform to programmaticaly author, schedule and monitor data pipelines.

Fission.io - Fission.io is a serverless framework for Kubernetes that supports many concepts such as event triggers, parallel execution, and statelessness.

Kestra.io - Infinitely scalable, event-driven, language-agnostic orchestration and scheduling platform to manage millions of workflows declaratively in code.

AWS Lambda - Automatic, event-driven compute service

Dagster - The cloud-native open source orchestrator for the whole development lifecycle, with integrated lineage and observability, a declarative programming model, and best-in-class testability.